cfgrib 0.8.5.0

Installation

The package is installed from PyPI with:

$ pip install cfgrib

$ sudo apt-get install libeccodes0

$ brew install eccodes

$ python -m cfgrib selfcheck
Found: ecCodes v2.7.0.
Your system is ready.

Usage

First, you need a well-formed GRIB file, if you don’t have one at hand you can download our ERA5 on pressure levels sample:

$ wget http://download.ecmwf.int/test-data/cfgrib/era5-levels-members.grib

>>> import cfgrib
>>> ds = cfgrib.Dataset.from_path('era5-levels-members.grib')
>>> ds.attributes['GRIB_edition']
1
>>> sorted(ds.dimensions.items())
[('air_pressure', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]
>>> sorted(ds.variables)
['air_pressure', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']
>>> var = ds.variables['t']
>>> var.dimensions
('number', 'time', 'air_pressure', 'latitude', 'longitude')
>>> var.data[:, :, :, :, :].mean()
262.92133
>>> ds = cfgrib.Dataset.from_path('era5-levels-members.grib')
>>> ds.attributes['GRIB_edition']
1
>>> sorted(ds.dimensions.items())
[('air_pressure', 2), ('latitude', 61), ('longitude', 120), ('number', 10), ('time', 4)]
>>> sorted(ds.variables)
['air_pressure', 'latitude', 'longitude', 'number', 'step', 't', 'time', 'valid_time', 'z']
>>> var = ds.variables['t']
>>> var.dimensions
('number', 'time', 'air_pressure', 'latitude', 'longitude')
>>> var.data[:, :, :, :, :].mean()
262.92133

$ pip install xarray

>>> ds = cfgrib.open_dataset('era5-levels-members.grib')
>>> ds
<xarray.Dataset>
Dimensions:       (air_pressure: 2, latitude: 61, longitude: 120, number: 10, time: 4)
Coordinates:
  * number        (number) int64 0 1 2 3 4 5 6 7 8 9
  * time          (time) datetime64[ns] 2017-01-01 2017-01-01T12:00:00 ...
    step          timedelta64[ns] ...
  * air_pressure  (air_pressure) float64 850.0 500.0
  * latitude      (latitude) float64 90.0 87.0 84.0 81.0 78.0 75.0 72.0 69.0 ...
  * longitude     (longitude) float64 0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 ...
    valid_time    (time) datetime64[ns] ...
Data variables:
    z             (number, time, air_pressure, latitude, longitude) float32 ...
    t             (number, time, air_pressure, latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            1
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    history:                 GRIB to CDM+CF via cfgrib-0.8.../ecCodes-2...

Advanced usage

cfgrib.Dataset can open a GRIB file only if all the messages with the same shortName can be represented as a single cfgrib.Variable hypercube. For example, a variable t cannot have both isobaricInhPa and hybrid typeOfLevel’s, as this would result in multiple hypercubes for variable t. Opening a non-conformant GRIB file will fail with a ValueError: multiple values for unique attribute... error message, see #2.

Furthermore if different cfgrib.Variable’s depend on the same coordinate, the values of the coordinate must match exactly. For example, if variables t and z share the same step coordinate, they must both have exactly the same set of steps. Opening a non-conformant GRIB file will fail with a ValueError: key present and new value is different... error message, see #13.

In most cases you can handle complex GRIB files containing heterogeneous messages by using the filter_by_keys keyword to select which GRIB messages belong to a well formed set of hypercubes.

For example to open US National Weather Service complex GRIB2 files you can use:

>>> cfgrib.open_dataset('nam.t00z.awp21100.tm00.grib2',
...              filter_by_keys={'typeOfLevel': 'surface', 'stepType': 'instant'})
<xarray.Dataset>
Dimensions:     (x: 93, y: 65)
Coordinates:
    time        datetime64[ns] ...
    step        timedelta64[ns] ...
    surface     int64 ...
    latitude    (y, x) float64 ...
    longitude   (y, x) float64 ...
    valid_time  datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    gust        (y, x) float32 ...
    sp          (y, x) float32 ...
    orog        (y, x) float32 ...
    csnow       (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    history:                 GRIB to CDM+CF via cfgrib-0.8.../ecCodes-2...
>>> cfgrib.open_dataset('nam.t00z.awp21100.tm00.grib2',
...              filter_by_keys={'typeOfLevel': 'heightAboveGround', 'level': 2})
<xarray.Dataset>
Dimensions:            (x: 93, y: 65)
Coordinates:
    time               datetime64[ns] ...
    step               timedelta64[ns] ...
    heightAboveGround  int64 ...
    latitude           (y, x) float64 ...
    longitude          (y, x) float64 ...
    valid_time         datetime64[ns] ...
Dimensions without coordinates: x, y
Data variables:
    t2m                (y, x) float32 ...
    r2                 (y, x) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             kwbc
    GRIB_centreDescription:  US National Weather Service - NCEP...
    GRIB_subCentre:          0
    history:                 GRIB to CDM+CF via cfgrib-0.8.../ecCodes-2...

Advanced Write Usage

Please note that write support is highly experimental. Only xarray.Dataset’s with the coordinates names matching the GRIB coordinates can be saved:

>>> ds = cfgrib.open_dataset('era5-levels-members.grib')
>>> ds
<xarray.Dataset>
Dimensions:       (air_pressure: 2, latitude: 61, longitude: 120, number: 10, time: 4)
Coordinates:
  * number        (number) int64 0 1 2 3 4 5 6 7 8 9
  * time          (time) datetime64[ns] 2017-01-01 2017-01-01T12:00:00 ...
    step          timedelta64[ns] ...
  * air_pressure  (air_pressure) float64 850.0 500.0
  * latitude      (latitude) float64 90.0 87.0 84.0 81.0 78.0 75.0 72.0 69.0 ...
  * longitude     (longitude) float64 0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 ...
    valid_time    (time) datetime64[ns] ...
Data variables:
    z             (number, time, air_pressure, latitude, longitude) float32 ...
    t             (number, time, air_pressure, latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            1
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    history:                 GRIB to CDM+CF via cfgrib-0.8.../ecCodes-2...
>>> cfgrib.to_grib(ds, 'out1.grib', grib_keys={'centre': 'ecmf'})
>>> cfgrib.open_dataset('out1.grib')
<xarray.Dataset>
Dimensions:       (air_pressure: 2, latitude: 61, longitude: 120, number: 10, time: 4)
Coordinates:
  * number        (number) int64 0 1 2 3 4 5 6 7 8 9
  * time          (time) datetime64[ns] 2017-01-01 2017-01-01T12:00:00 ...
    step          timedelta64[ns] ...
  * air_pressure  (air_pressure) float64 850.0 500.0
  * latitude      (latitude) float64 90.0 87.0 84.0 81.0 78.0 75.0 72.0 69.0 ...
  * longitude     (longitude) float64 0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 ...
    valid_time    (time) datetime64[ns] ...
Data variables:
    z             (number, time, air_pressure, latitude, longitude) float32 ...
    t             (number, time, air_pressure, latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    history:                 GRIB to CDM+CF via cfgrib-0.8.../ecCodes-2...

Per-variable GRIB keys can be set by setting the attrs variable with key prefixed by GRIB_, for example:

>>> import numpy as np
>>> import xarray as xr
>>> ds2 = xr.DataArray(
...     np.zeros((5, 6)) + 300.,
...     coords=[
...         np.linspace(90., -90., 5),
...         np.linspace(0., 360., 6, endpoint=False),
...     ],
...     dims=['latitude', 'longitude'],
... ).to_dataset(name='skin_temperature')
>>> ds2.skin_temperature.attrs['GRIB_shortName'] = 'skt'
>>> cfgrib.to_grib(ds2, 'out2.grib')
>>> cfgrib.open_dataset('out2.grib')
<xarray.Dataset>
Dimensions:     (latitude: 5, longitude: 6)
Coordinates:
    time        datetime64[ns] ...
    step        timedelta64[ns] ...
    surface     int64 ...
  * latitude    (latitude) float64 90.0 45.0 0.0 -45.0 -90.0
  * longitude   (longitude) float64 0.0 60.0 120.0 180.0 240.0 300.0
    valid_time  datetime64[ns] ...
Data variables:
    skt         (latitude, longitude) float32 ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             consensus
    GRIB_centreDescription:  Consensus
    GRIB_subCentre:          0
    history:                 GRIB to CDM+CF via cfgrib-0.8.../ecCodes-2...

Contributing

The main repository is hosted on GitHub, testing, bug reports and contributions are highly welcomed and appreciated:

https://github.com/ecmwf/cfgrib

Please see the CONTRIBUTING.rst document for the best way to help.

Lead developer:

• Alessandro Amici - B-Open

Main contributors:

• Baudouin Raoult - ECMWF

• Aureliana Barghini - B-Open

• Iain Russell - ECMWF

• Leonardo Barcaroli - B-Open

See also the list of contributors who participated in this project.

License

Copyright 2017-2018 European Centre for Medium-Range Weather Forecasts (ECMWF).

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at: http://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.